Market Segmentation

IMPORT LIBRARIES AND DATASETS

This need to execute in tensor flow environment¶

Import Libraries

In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler, normalize
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
import os
import warnings
warnings.filterwarnings('ignore')

Read Sales Data, display top 5 records

In [3]:
creditcard_df = pd.read_csv("C:\\Users\\Asus\\Desktop\\Data Science\\TOPMENTOR-DS\\Capstone_project\\Projects\\Project -5 Marketting Department\\Marketing_data.csv")
display (creditcard_df )
CUST_ID BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
0 C10001 40.900749 0.818182 95.40 0.00 95.40 0.000000 0.166667 0.000000 0.083333 0.000000 0 2 1000.0 201.802084 139.509787 0.000000 12
1 C10002 3202.467416 0.909091 0.00 0.00 0.00 6442.945483 0.000000 0.000000 0.000000 0.250000 4 0 7000.0 4103.032597 1072.340217 0.222222 12
2 C10003 2495.148862 1.000000 773.17 773.17 0.00 0.000000 1.000000 1.000000 0.000000 0.000000 0 12 7500.0 622.066742 627.284787 0.000000 12
3 C10004 1666.670542 0.636364 1499.00 1499.00 0.00 205.788017 0.083333 0.083333 0.000000 0.083333 1 1 7500.0 0.000000 NaN 0.000000 12
4 C10005 817.714335 1.000000 16.00 16.00 0.00 0.000000 0.083333 0.083333 0.000000 0.000000 0 1 1200.0 678.334763 244.791237 0.000000 12
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
8945 C19186 28.493517 1.000000 291.12 0.00 291.12 0.000000 1.000000 0.000000 0.833333 0.000000 0 6 1000.0 325.594462 48.886365 0.500000 6
8946 C19187 19.183215 1.000000 300.00 0.00 300.00 0.000000 1.000000 0.000000 0.833333 0.000000 0 6 1000.0 275.861322 NaN 0.000000 6
8947 C19188 23.398673 0.833333 144.40 0.00 144.40 0.000000 0.833333 0.000000 0.666667 0.000000 0 5 1000.0 81.270775 82.418369 0.250000 6
8948 C19189 13.457564 0.833333 0.00 0.00 0.00 36.558778 0.000000 0.000000 0.000000 0.166667 2 0 500.0 52.549959 55.755628 0.250000 6
8949 C19190 372.708075 0.666667 1093.25 1093.25 0.00 127.040008 0.666667 0.666667 0.000000 0.333333 2 23 1200.0 63.165404 88.288956 0.000000 6

8950 rows × 18 columns

Data Set Details - Info

In [5]:
creditcard_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8950 entries, 0 to 8949
Data columns (total 18 columns):
 #   Column                            Non-Null Count  Dtype  
---  ------                            --------------  -----  
 0   CUST_ID                           8950 non-null   object 
 1   BALANCE                           8950 non-null   float64
 2   BALANCE_FREQUENCY                 8950 non-null   float64
 3   PURCHASES                         8950 non-null   float64
 4   ONEOFF_PURCHASES                  8950 non-null   float64
 5   INSTALLMENTS_PURCHASES            8950 non-null   float64
 6   CASH_ADVANCE                      8950 non-null   float64
 7   PURCHASES_FREQUENCY               8950 non-null   float64
 8   ONEOFF_PURCHASES_FREQUENCY        8950 non-null   float64
 9   PURCHASES_INSTALLMENTS_FREQUENCY  8950 non-null   float64
 10  CASH_ADVANCE_FREQUENCY            8950 non-null   float64
 11  CASH_ADVANCE_TRX                  8950 non-null   int64  
 12  PURCHASES_TRX                     8950 non-null   int64  
 13  CREDIT_LIMIT                      8949 non-null   float64
 14  PAYMENTS                          8950 non-null   float64
 15  MINIMUM_PAYMENTS                  8637 non-null   float64
 16  PRC_FULL_PAYMENT                  8950 non-null   float64
 17  TENURE                            8950 non-null   int64  
dtypes: float64(14), int64(3), object(1)
memory usage: 1.2+ MB

Data Set Details – Describe

In [6]:
creditcard_df.describe()
Out[6]:
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
count 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8949.000000 8950.000000 8637.000000 8950.000000 8950.000000
mean 1564.474828 0.877271 1003.204834 592.437371 411.067645 978.871112 0.490351 0.202458 0.364437 0.135144 3.248827 14.709832 4494.449450 1733.143852 864.206542 0.153715 11.517318
std 2081.531879 0.236904 2136.634782 1659.887917 904.338115 2097.163877 0.401371 0.298336 0.397448 0.200121 6.824647 24.857649 3638.815725 2895.063757 2372.446607 0.292499 1.338331
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 50.000000 0.000000 0.019163 0.000000 6.000000
25% 128.281915 0.888889 39.635000 0.000000 0.000000 0.000000 0.083333 0.000000 0.000000 0.000000 0.000000 1.000000 1600.000000 383.276166 169.123707 0.000000 12.000000
50% 873.385231 1.000000 361.280000 38.000000 89.000000 0.000000 0.500000 0.083333 0.166667 0.000000 0.000000 7.000000 3000.000000 856.901546 312.343947 0.000000 12.000000
75% 2054.140036 1.000000 1110.130000 577.405000 468.637500 1113.821139 0.916667 0.300000 0.750000 0.222222 4.000000 17.000000 6500.000000 1901.134317 825.485459 0.142857 12.000000
max 19043.138560 1.000000 49039.570000 40761.250000 22500.000000 47137.211760 1.000000 1.000000 1.000000 1.500000 123.000000 358.000000 30000.000000 50721.483360 76406.207520 1.000000 12.000000

Customer with maximum 'ONEOFF_PURCHASES'

In [8]:
# Check who made one off purchase of $40761 ie maximum ONEOFF_PURCHASES 
creditcard_df[creditcard_df['ONEOFF_PURCHASES'] == 40761.25]
Out[8]:
CUST_ID BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
550 C10574 11547.52001 1.0 49039.57 40761.25 8278.32 558.166886 1.0 1.0 0.916667 0.083333 1 101 22500.0 46930.59824 2974.069421 0.25 12

Customer with maximum Cash Advance

In [11]:
creditcard_df['CASH_ADVANCE'].max()
Out[11]:
47137.21176
In [12]:
# Check who made cash advance of $47137
# This customer made 123 cash advance transactions
# Never paid credit card in full
creditcard_df[creditcard_df['CASH_ADVANCE'] == 47137.211760000006]
Out[12]:
CUST_ID BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE

VISUALIZE AND EXPLORE DATASET

Check missing values

In [13]:
# Check for missing Data 
creditcard_df.isnull().sum()
Out[13]:
CUST_ID                               0
BALANCE                               0
BALANCE_FREQUENCY                     0
PURCHASES                             0
ONEOFF_PURCHASES                      0
INSTALLMENTS_PURCHASES                0
CASH_ADVANCE                          0
PURCHASES_FREQUENCY                   0
ONEOFF_PURCHASES_FREQUENCY            0
PURCHASES_INSTALLMENTS_FREQUENCY      0
CASH_ADVANCE_FREQUENCY                0
CASH_ADVANCE_TRX                      0
PURCHASES_TRX                         0
CREDIT_LIMIT                          1
PAYMENTS                              0
MINIMUM_PAYMENTS                    313
PRC_FULL_PAYMENT                      0
TENURE                                0
dtype: int64
In [20]:
# Heat map for missing data 
sns.heatmap(creditcard_df.isnull(), yticklabels = False, cbar = False, cmap="Blues")
Out[20]:
<Axes: >
No description has been provided for this image

Fill the missing values - Column MINIMUM_PAYMENTS and CREDIT_LIMIT

In [23]:
# Fill up the missing elements with mean of the 'MINIMUM_PAYMENT' 
creditcard_df.loc[(creditcard_df['MINIMUM_PAYMENTS'].isnull() == True), 'MINIMUM_PAYMENTS'] = creditcard_df['MINIMUM_PAYMENTS'].mean()
In [24]:
# Fill up the missing elements with mean of the 'CREDIT_LIMIT' 
creditcard_df.loc[(creditcard_df['CREDIT_LIMIT'].isnull() == True), 'CREDIT_LIMIT'] = creditcard_df['CREDIT_LIMIT'].mean()

Check for Duplicate Data

In [25]:
creditcard_df.duplicated().sum()
Out[25]:
0

Check missing values

In [27]:
creditcard_df.isnull().sum()
Out[27]:
CUST_ID                             0
BALANCE                             0
BALANCE_FREQUENCY                   0
PURCHASES                           0
ONEOFF_PURCHASES                    0
INSTALLMENTS_PURCHASES              0
CASH_ADVANCE                        0
PURCHASES_FREQUENCY                 0
ONEOFF_PURCHASES_FREQUENCY          0
PURCHASES_INSTALLMENTS_FREQUENCY    0
CASH_ADVANCE_FREQUENCY              0
CASH_ADVANCE_TRX                    0
PURCHASES_TRX                       0
CREDIT_LIMIT                        0
PAYMENTS                            0
MINIMUM_PAYMENTS                    0
PRC_FULL_PAYMENT                    0
TENURE                              0
dtype: int64

Missing values Heatmap

In [28]:
 sns.heatmap(creditcard_df.isnull(), yticklabels = False, cbar = False, cmap="Blues")
Out[28]:
<Axes: >
No description has been provided for this image
In [29]:
# Check for duplicated entries in the data
creditcard_df.duplicated().sum()
Out[29]:
0

Drop customer id column

In [30]:
# Drop Customer ID since it has no meaning here 
creditcard_df.drop("CUST_ID", axis = 1, inplace= True)
display(creditcard_df)
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
0 40.900749 0.818182 95.40 0.00 95.40 0.000000 0.166667 0.000000 0.083333 0.000000 0 2 1000.0 201.802084 139.509787 0.000000 12
1 3202.467416 0.909091 0.00 0.00 0.00 6442.945483 0.000000 0.000000 0.000000 0.250000 4 0 7000.0 4103.032597 1072.340217 0.222222 12
2 2495.148862 1.000000 773.17 773.17 0.00 0.000000 1.000000 1.000000 0.000000 0.000000 0 12 7500.0 622.066742 627.284787 0.000000 12
3 1666.670542 0.636364 1499.00 1499.00 0.00 205.788017 0.083333 0.083333 0.000000 0.083333 1 1 7500.0 0.000000 864.206542 0.000000 12
4 817.714335 1.000000 16.00 16.00 0.00 0.000000 0.083333 0.083333 0.000000 0.000000 0 1 1200.0 678.334763 244.791237 0.000000 12
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
8945 28.493517 1.000000 291.12 0.00 291.12 0.000000 1.000000 0.000000 0.833333 0.000000 0 6 1000.0 325.594462 48.886365 0.500000 6
8946 19.183215 1.000000 300.00 0.00 300.00 0.000000 1.000000 0.000000 0.833333 0.000000 0 6 1000.0 275.861322 864.206542 0.000000 6
8947 23.398673 0.833333 144.40 0.00 144.40 0.000000 0.833333 0.000000 0.666667 0.000000 0 5 1000.0 81.270775 82.418369 0.250000 6
8948 13.457564 0.833333 0.00 0.00 0.00 36.558778 0.000000 0.000000 0.000000 0.166667 2 0 500.0 52.549959 55.755628 0.250000 6
8949 372.708075 0.666667 1093.25 1093.25 0.00 127.040008 0.666667 0.666667 0.000000 0.333333 2 23 1200.0 63.165404 88.288956 0.000000 6

8950 rows × 17 columns

Number of Columns

In [31]:
n = len(creditcard_df.columns)
display(n)

17

Display Column Names

In [32]:
display (creditcard_df.columns)

Index(['BALANCE', 'BALANCE_FREQUENCY', 'PURCHASES', 'ONEOFF_PURCHASES',
       'INSTALLMENTS_PURCHASES', 'CASH_ADVANCE', 'PURCHASES_FREQUENCY',
       'ONEOFF_PURCHASES_FREQUENCY', 'PURCHASES_INSTALLMENTS_FREQUENCY',
       'CASH_ADVANCE_FREQUENCY', 'CASH_ADVANCE_TRX', 'PURCHASES_TRX',
       'CREDIT_LIMIT', 'PAYMENTS', 'MINIMUM_PAYMENTS', 'PRC_FULL_PAYMENT',
       'TENURE'],
      dtype='object')

Create dist plot

In [34]:
# distplot combines the matplotlib.hist function with seaborn kdeplot()
# KDE Plot represents the Kernel Density Estimate
# KDE is used for visualizing the Probability Density of a continuous variable. 
# KDE demonstrates the probability density at different values in a continuous variable. 

# Mean of balance is $1500
# 'Balance_Frequency' for most customers is updated frequently ~1
# For 'PURCHASES_FREQUENCY', there are two distinct group of customers
# For 'ONEOFF_PURCHASES_FREQUENCY' and 'PURCHASES_INSTALLMENT_FREQUENCY' most users don't do one off puchases or installment purchases frequently 
# Very small number of customers pay their balance in full 'PRC_FULL_PAYMENT'~0
# Credit limit average is around $4500
# Most customers are ~11 years tenure

plt.figure(figsize=(20,80))
for i in range(len(creditcard_df.columns)):
  plt.subplot(17, 1, i+1)
  sns.distplot(creditcard_df[creditcard_df.columns[i]], kde_kws={"color": "b", "lw": 3, "label": "KDE"}, hist_kws={"color": "g"})
  plt.title(creditcard_df.columns[i])

plt.tight_layout()
No description has been provided for this image

Create Pair Plot

In [36]:
sns.pairplot(creditcard_df)
# Correlation between 'PURCHASES' and ONEOFF_PURCHASES & INSTALMENT_PURCHASES 
# Trend between 'PURCHASES' and 'CREDIT_LIMIT' & 'PAYMENTS'
Out[36]:
<seaborn.axisgrid.PairGrid at 0x25592da74d0>
No description has been provided for this image

Display Co relation Matrix

In [37]:
correlations = creditcard_df.corr()
display (correlations )
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
BALANCE 1.000000 0.322412 0.181261 0.164350 0.126469 0.496692 -0.077944 0.073166 -0.063186 0.449218 0.385152 0.154338 0.531267 0.322802 0.394282 -0.318959 0.072692
BALANCE_FREQUENCY 0.322412 1.000000 0.133674 0.104323 0.124292 0.099388 0.229715 0.202415 0.176079 0.191873 0.141555 0.189626 0.095795 0.065008 0.114249 -0.095082 0.119776
PURCHASES 0.181261 0.133674 1.000000 0.916845 0.679896 -0.051474 0.393017 0.498430 0.315567 -0.120143 -0.067175 0.689561 0.356959 0.603264 0.093515 0.180379 0.086288
ONEOFF_PURCHASES 0.164350 0.104323 0.916845 1.000000 0.330622 -0.031326 0.264937 0.524891 0.127729 -0.082628 -0.046212 0.545523 0.319721 0.567292 0.048597 0.132763 0.064150
INSTALLMENTS_PURCHASES 0.126469 0.124292 0.679896 0.330622 1.000000 -0.064244 0.442418 0.214042 0.511351 -0.132318 -0.073999 0.628108 0.256496 0.384084 0.131687 0.182569 0.086143
CASH_ADVANCE 0.496692 0.099388 -0.051474 -0.031326 -0.064244 1.000000 -0.215507 -0.086754 -0.177070 0.628522 0.656498 -0.075850 0.303983 0.453238 0.139223 -0.152935 -0.068312
PURCHASES_FREQUENCY -0.077944 0.229715 0.393017 0.264937 0.442418 -0.215507 1.000000 0.501343 0.862934 -0.308478 -0.203478 0.568430 0.119778 0.103464 0.002976 0.305802 0.061506
ONEOFF_PURCHASES_FREQUENCY 0.073166 0.202415 0.498430 0.524891 0.214042 -0.086754 0.501343 1.000000 0.142329 -0.111716 -0.069088 0.544869 0.295030 0.243537 -0.029963 0.157531 0.082466
PURCHASES_INSTALLMENTS_FREQUENCY -0.063186 0.176079 0.315567 0.127729 0.511351 -0.177070 0.862934 0.142329 1.000000 -0.262958 -0.169207 0.529975 0.060752 0.085551 0.029590 0.250087 0.073275
CASH_ADVANCE_FREQUENCY 0.449218 0.191873 -0.120143 -0.082628 -0.132318 0.628522 -0.308478 -0.111716 -0.262958 1.000000 0.799561 -0.131168 0.132616 0.183192 0.097898 -0.249773 -0.133372
CASH_ADVANCE_TRX 0.385152 0.141555 -0.067175 -0.046212 -0.073999 0.656498 -0.203478 -0.069088 -0.169207 0.799561 1.000000 -0.066157 0.149699 0.255278 0.109185 -0.169784 -0.043421
PURCHASES_TRX 0.154338 0.189626 0.689561 0.545523 0.628108 -0.075850 0.568430 0.544869 0.529975 -0.131168 -0.066157 1.000000 0.272877 0.370832 0.095858 0.162066 0.121874
CREDIT_LIMIT 0.531267 0.095795 0.356959 0.319721 0.256496 0.303983 0.119778 0.295030 0.060752 0.132616 0.149699 0.272877 1.000000 0.421852 0.125134 0.055671 0.139034
PAYMENTS 0.322802 0.065008 0.603264 0.567292 0.384084 0.453238 0.103464 0.243537 0.085551 0.183192 0.255278 0.370832 0.421852 1.000000 0.125046 0.112138 0.106136
MINIMUM_PAYMENTS 0.394282 0.114249 0.093515 0.048597 0.131687 0.139223 0.002976 -0.029963 0.029590 0.097898 0.109185 0.095858 0.125134 0.125046 1.000000 -0.139674 0.057257
PRC_FULL_PAYMENT -0.318959 -0.095082 0.180379 0.132763 0.182569 -0.152935 0.305802 0.157531 0.250087 -0.249773 -0.169784 0.162066 0.055671 0.112138 -0.139674 1.000000 -0.016486
TENURE 0.072692 0.119776 0.086288 0.064150 0.086143 -0.068312 0.061506 0.082466 0.073275 -0.133372 -0.043421 0.121874 0.139034 0.106136 0.057257 -0.016486 1.000000

Create Heat Map

In [38]:
f, ax = plt.subplots(figsize = (20, 20))
sns.heatmap(correlations, annot = True)

# 'PURCHASES' have high correlation between one-off purchases, 'installment purchases, purchase transactions, credit limit and payments. 
# Strong Positive Correlation between 'PURCHASES_FREQUENCY' and 'PURCHASES_INSTALLMENT_FREQUENCY'
Out[38]:
<Axes: >
No description has been provided for this image

OPTIMAL NUMBER OF CLUSTERS

Display the Data Set

In [41]:
display (creditcard_df)
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
0 40.900749 0.818182 95.40 0.00 95.40 0.000000 0.166667 0.000000 0.083333 0.000000 0 2 1000.0 201.802084 139.509787 0.000000 12
1 3202.467416 0.909091 0.00 0.00 0.00 6442.945483 0.000000 0.000000 0.000000 0.250000 4 0 7000.0 4103.032597 1072.340217 0.222222 12
2 2495.148862 1.000000 773.17 773.17 0.00 0.000000 1.000000 1.000000 0.000000 0.000000 0 12 7500.0 622.066742 627.284787 0.000000 12
3 1666.670542 0.636364 1499.00 1499.00 0.00 205.788017 0.083333 0.083333 0.000000 0.083333 1 1 7500.0 0.000000 864.206542 0.000000 12
4 817.714335 1.000000 16.00 16.00 0.00 0.000000 0.083333 0.083333 0.000000 0.000000 0 1 1200.0 678.334763 244.791237 0.000000 12
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
8945 28.493517 1.000000 291.12 0.00 291.12 0.000000 1.000000 0.000000 0.833333 0.000000 0 6 1000.0 325.594462 48.886365 0.500000 6
8946 19.183215 1.000000 300.00 0.00 300.00 0.000000 1.000000 0.000000 0.833333 0.000000 0 6 1000.0 275.861322 864.206542 0.000000 6
8947 23.398673 0.833333 144.40 0.00 144.40 0.000000 0.833333 0.000000 0.666667 0.000000 0 5 1000.0 81.270775 82.418369 0.250000 6
8948 13.457564 0.833333 0.00 0.00 0.00 36.558778 0.000000 0.000000 0.000000 0.166667 2 0 500.0 52.549959 55.755628 0.250000 6
8949 372.708075 0.666667 1093.25 1093.25 0.00 127.040008 0.666667 0.666667 0.000000 0.333333 2 23 1200.0 63.165404 88.288956 0.000000 6

8950 rows × 17 columns

Standardise the Data

In [42]:
# Let's scale the data first
scaler = StandardScaler()
creditcard_df_scaled = scaler.fit_transform(creditcard_df)

Display the Shape

In [43]:
creditcard_df_scaled.shape
Out[43]:
(8950, 17)

Display the Shape

In [44]:
display(pd.DataFrame(creditcard_df_scaled))
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0 -0.731989 -0.249434 -0.424900 -0.356934 -0.349079 -0.466786 -0.806490 -0.678661 -0.707313 -0.675349 -0.476070 -0.511333 -0.960433 -0.528979 -3.109675e-01 -0.525551 0.360680
1 0.786961 0.134325 -0.469552 -0.356934 -0.454576 2.605605 -1.221758 -0.678661 -0.916995 0.573963 0.110074 -0.591796 0.688639 0.818642 8.931021e-02 0.234227 0.360680
2 0.447135 0.518084 -0.107668 0.108889 -0.454576 -0.466786 1.269843 2.673451 -0.916995 -0.675349 -0.476070 -0.109020 0.826062 -0.383805 -1.016632e-01 -0.525551 0.360680
3 0.049099 -1.016953 0.232058 0.546189 -0.454576 -0.368653 -1.014125 -0.399319 -0.916995 -0.258913 -0.329534 -0.551565 0.826062 -0.598688 4.878305e-17 -0.525551 0.360680
4 -0.358775 0.518084 -0.462063 -0.347294 -0.454576 -0.466786 -1.014125 -0.399319 -0.916995 -0.675349 -0.476070 -0.551565 -0.905464 -0.364368 -2.657913e-01 -0.525551 0.360680
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
8945 -0.737950 0.518084 -0.333293 -0.356934 -0.132643 -0.466786 1.269843 -0.678661 1.179833 -0.675349 -0.476070 -0.350408 -0.960433 -0.486217 -3.498541e-01 1.183951 -4.122768
8946 -0.742423 0.518084 -0.329136 -0.356934 -0.122823 -0.466786 1.269843 -0.678661 1.179833 -0.675349 -0.476070 -0.350408 -0.960433 -0.503396 4.878305e-17 -0.525551 -4.122768
8947 -0.740398 -0.185477 -0.401965 -0.356934 -0.294893 -0.466786 0.854576 -0.678661 0.760469 -0.675349 -0.476070 -0.390639 -0.960433 -0.570615 -3.354655e-01 0.329200 -4.122768
8948 -0.745174 -0.185477 -0.469552 -0.356934 -0.454576 -0.449352 -1.221758 -0.678661 -0.916995 0.157527 -0.182998 -0.591796 -1.097856 -0.580536 -3.469065e-01 0.329200 -4.122768
8949 -0.572575 -0.889033 0.042146 0.301732 -0.454576 -0.406205 0.439310 1.556082 -0.916995 0.990398 -0.182998 0.333524 -0.905464 -0.576869 -3.329464e-01 -0.525551 -4.122768

8950 rows × 17 columns

Create Elbow Graph

In [45]:
scores_1 = []

range_values = range(1, 20)

for i in range_values:
  kmeans = KMeans(n_clusters = i)
  kmeans.fit(creditcard_df_scaled)
  scores_1.append(kmeans.inertia_) 

plt.plot(scores_1, 'bx-')
plt.title('Finding the right number of clusters')
plt.xlabel('Clusters')
plt.ylabel('Scores') 
plt.show()

# From this we can observe that, 4th cluster seems to be forming the elbow of the curve. 
# However, the values does not reduce linearly until 8th cluster. 
# Let's choose the number of clusters to be 7.
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APPLY K -Means

In [46]:
kmeans = KMeans(8)
kmeans.fit(creditcard_df_scaled)
labels = kmeans.labels_

Number of Clusters

In [47]:
kmeans.cluster_centers_.shape
Out[47]:
(8, 17)

Cluster Canters

In [48]:
cluster_centers = pd.DataFrame(data = kmeans.cluster_centers_, columns = [creditcard_df.columns])
display(cluster_centers )
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
0 0.015135 0.401676 -0.360086 -0.239716 -0.410893 -0.087612 -0.883632 -0.396319 -0.788326 0.117270 -0.021254 -0.490471 -0.300035 -0.248018 -0.007566 -0.451779 0.271649
1 -0.689836 0.138850 0.145397 0.012575 0.320215 -0.448934 0.916375 0.369233 0.737875 -0.643047 -0.458441 0.239925 0.144182 -0.106995 -0.292148 2.167427 0.188918
2 -0.699800 -2.200786 -0.322361 -0.242809 -0.315996 -0.314720 -0.576542 -0.457591 -0.449006 -0.512310 -0.369474 -0.426412 -0.193528 -0.208250 -0.253790 0.114249 0.197074
3 -0.154754 0.400280 0.042747 -0.048076 0.189318 -0.324006 0.936685 0.253708 0.844046 -0.391518 -0.314284 0.252429 -0.174722 -0.157935 0.023235 -0.336341 0.288790
4 0.767941 0.473271 2.070780 1.735430 1.708084 -0.212854 1.155205 1.734325 1.080259 -0.309542 -0.227256 2.444224 1.159369 1.139322 0.332806 0.218624 0.316643
5 1.923051 0.337717 11.212042 10.600367 7.033118 0.419625 1.046983 1.915501 0.981334 -0.258912 0.061229 5.362438 3.044064 8.098975 1.120318 1.110132 0.310863
6 1.661133 0.392290 -0.215330 -0.147700 -0.237854 2.016433 -0.470405 -0.175897 -0.429492 1.929081 1.940767 -0.264191 1.018817 0.821953 0.503051 -0.392101 0.068281
7 -0.332825 -0.344129 -0.287118 -0.205018 -0.301852 0.072498 -0.196537 -0.269970 -0.234654 0.313908 0.002881 -0.385982 -0.563404 -0.392199 -0.205867 -0.035486 -3.194122

Inverse Transformation- Convert to original Data

In [49]:
# In order to understand what these numbers mean, perform inverse transformation
cluster_centers = scaler.inverse_transform(cluster_centers)
cluster_centers = pd.DataFrame(data = cluster_centers, columns = [creditcard_df.columns])
display(cluster_centers)

# First Customers cluster (Transactors): Those are customers who pay least amount of intrerest charges and careful with their money, Cluster with lowest balance ($104) and cash advance ($303), Percentage of full payment = 23%
# Second customers cluster (revolvers) who use credit card as a loan (most lucrative sector): highest balance ($5000) and cash advance (~$5000), low purchase frequency, high cash advance frequency (0.5), high cash advance transactions (16) and low percentage of full payment (3%)
# Third customer cluster (VIP/Prime): high credit limit $16K and highest percentage of full payment, target for increase credit limit and increase spending habits
# Fourth customer cluster (low tenure): these are customers with low tenure (7 years), low balance
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
0 1595.977104 0.972424 233.875047 194.558340 39.502124 795.145112 0.135706 0.084228 0.051137 0.158611 3.103784 2.518546 3402.797552 1015.155052 846.573757 0.021577 11.880854
1 128.638825 0.910163 1313.848260 613.309356 700.634479 37.436073 0.858136 0.312607 0.657688 0.006464 0.120301 20.673469 5019.043550 1423.404727 183.368356 0.787650 11.770140
2 107.899326 0.355925 314.474781 189.423691 125.316327 318.889451 0.258957 0.065949 0.185991 0.032626 0.727435 4.110813 3790.316751 1130.279788 272.759429 0.187131 11.781055
3 1242.368480 0.972093 1094.534440 512.640782 582.265675 299.414996 0.866288 0.278144 0.699883 0.056797 1.104070 20.984274 3858.738635 1275.938111 918.355586 0.055341 11.903793
4 3162.878330 0.989384 5427.457293 3472.895175 1955.667090 532.505889 0.953990 0.719840 0.793760 0.073202 1.697974 75.464088 8712.707182 5031.368305 1639.796192 0.217658 11.941068
5 5567.142164 0.957273 24957.905000 18186.875667 6771.029333 1858.844605 0.910556 0.773889 0.754444 0.083333 3.666667 148.000000 15570.000000 25178.882690 3475.059479 0.478409 11.933333
6 5021.982324 0.970201 543.148341 347.285229 195.979439 5207.426348 0.301554 0.149984 0.193746 0.521173 16.493135 8.143021 8201.320990 4112.616849 2036.545744 0.039032 11.608696
7 871.727207 0.795750 389.773601 252.149727 138.106190 1130.903517 0.411471 0.121920 0.271180 0.197960 3.268489 5.115756 2444.556993 597.766825 384.441450 0.143336 7.242765

Display Cluster Details

In [50]:
display(labels.shape) # Labels associated to each data point
display (labels.max())
display (labels.min())
         

(8950,)
7
0

Display Cluster numbers

In [51]:
y_kmeans = kmeans.fit_predict(creditcard_df_scaled)
display(y_kmeans)

array([3, 5, 2, ..., 1, 3, 3])

Concatenate the clusters labels to our original data frame

In [52]:
# concatenate the clusters labels to our original dataframe
creditcard_df_cluster = pd.concat([creditcard_df, pd.DataFrame({'cluster':labels})], axis = 1)
creditcard_df_cluster.head()
Out[52]:
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE cluster
0 40.900749 0.818182 95.40 0.00 95.4 0.000000 0.166667 0.000000 0.083333 0.000000 0 2 1000.0 201.802084 139.509787 0.000000 12 0
1 3202.467416 0.909091 0.00 0.00 0.0 6442.945483 0.000000 0.000000 0.000000 0.250000 4 0 7000.0 4103.032597 1072.340217 0.222222 12 6
2 2495.148862 1.000000 773.17 773.17 0.0 0.000000 1.000000 1.000000 0.000000 0.000000 0 12 7500.0 622.066742 627.284787 0.000000 12 3
3 1666.670542 0.636364 1499.00 1499.00 0.0 205.788017 0.083333 0.083333 0.000000 0.083333 1 1 7500.0 0.000000 864.206542 0.000000 12 0
4 817.714335 1.000000 16.00 16.00 0.0 0.000000 0.083333 0.083333 0.000000 0.000000 0 1 1200.0 678.334763 244.791237 0.000000 12 0

Plot the histogram of various clusters

In [53]:
for i in creditcard_df.columns:
  plt.figure(figsize = (20,5))
  for j in range(8):
    plt.subplot(1,8,j+1)
    cluster = creditcard_df_cluster[creditcard_df_cluster['cluster'] == j]
    cluster[i].hist(bins = 20)
    plt.title('{}    \nCluster {} '.format(i,j))
  
  plt.show()
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PRINCIPAL COMPONENT ANALYSIS

In [54]:
pca = PCA(n_components=2)
principal_comp = pca.fit_transform(creditcard_df_scaled)
display(principal_comp)

array([[-1.68221979, -1.07645049],
       [-1.13829648,  2.50648419],
       [ 0.96968106, -0.38351597],
       ...,
       [-0.92620222, -1.81078897],
       [-2.33654929, -0.65797176],
       [-0.55642321, -0.4004678 ]])

Create a data frame with the two components

In [55]:
# Create a dataframe with the two components
pca_df = pd.DataFrame(data = principal_comp, columns =['pca1','pca2'])
pca_df.head()
Out[55]:
pca1 pca2
0 -1.682220 -1.076450
1 -1.138296 2.506484
2 0.969681 -0.383516
3 -0.873627 0.043167
4 -1.599433 -0.688581

Concatenate the clusters labels to the data frame

In [56]:
# Concatenate the clusters labels to the dataframe
pca_df = pd.concat([pca_df,pd.DataFrame({'cluster':labels})], axis = 1)
display(pca_df)
pca1 pca2 cluster
0 -1.682220 -1.076450 0
1 -1.138296 2.506484 6
2 0.969681 -0.383516 3
3 -0.873627 0.043167 0
4 -1.599433 -0.688581 0
... ... ... ...
8945 -0.359628 -2.016148 7
8946 -0.564367 -1.639129 7
8947 -0.926202 -1.810789 7
8948 -2.336549 -0.657972 7
8949 -0.556423 -0.400468 7

8950 rows × 3 columns

Number of records in each cluster

In [58]:
pca_df.value_counts(pca_df.cluster)
Out[58]:
cluster
0    2669
3    2162
2    1119
1     931
6     874
7     622
4     543
5      30
Name: count, dtype: int64

Plot the graph with two components

In [59]:
plt.figure(figsize=(20,10))
ax = sns.scatterplot(x="pca1", y="pca2", hue = "cluster", data = pca_df, palette =['red','green','blue','pink','yellow','gray','purple', 'black'])
plt.show()
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APPLY AUTO ENCODERS

Create the Layers

In [60]:
from tensorflow.keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, Dropout
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.initializers import glorot_uniform  # This is normalizer
from keras.optimizers import SGD

encoding_dim = 7

input_df = Input(shape=(17,)) # 17 Features 


# Glorot normal initializer (Xavier normal initializer) draws samples from a truncated normal distribution 

x = Dense(encoding_dim, activation='relu')(input_df)
x = Dense(500, activation='relu', kernel_initializer = 'glorot_uniform')(x)
x = Dense(500, activation='relu', kernel_initializer = 'glorot_uniform')(x)
x = Dense(2000, activation='relu', kernel_initializer = 'glorot_uniform')(x)

encoded = Dense(10, activation='relu', kernel_initializer = 'glorot_uniform')(x)

x = Dense(2000, activation='relu', kernel_initializer = 'glorot_uniform')(encoded)
x = Dense(500, activation='relu', kernel_initializer = 'glorot_uniform')(x)

decoded = Dense(17, kernel_initializer = 'glorot_uniform')(x)

# autoencoder
autoencoder = Model(input_df, decoded)

#encoder - used for our dimention reduction
encoder = Model(input_df, encoded)

autoencoder.compile(optimizer= 'adam', loss='mean_squared_error')

Display the shape

In [61]:
display (creditcard_df_scaled.shape)

(8950, 17)

Create Auto Encoder – Fit model

In [62]:
autoencoder.fit(creditcard_df_scaled, creditcard_df_scaled, batch_size = 128, epochs = 25,  verbose = 1)

Epoch 1/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 7s 47ms/step - loss: 0.6396
Epoch 2/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - loss: 0.3001
Epoch 3/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 3s 48ms/step - loss: 0.2196
Epoch 4/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 4s 50ms/step - loss: 0.1858
Epoch 5/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 4s 54ms/step - loss: 0.1746
Epoch 6/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 3s 19ms/step - loss: 0.1472
Epoch 7/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - loss: 0.1452
Epoch 8/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step - loss: 0.1281
Epoch 9/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 4s 53ms/step - loss: 0.1144
Epoch 10/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 4s 51ms/step - loss: 0.1117
Epoch 11/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 4s 51ms/step - loss: 0.1008
Epoch 12/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 3s 19ms/step - loss: 0.0933
Epoch 13/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 2s 22ms/step - loss: 0.0912
Epoch 14/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 3s 49ms/step - loss: 0.0834
Epoch 15/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 3s 19ms/step - loss: 0.0803
Epoch 16/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - loss: 0.0695
Epoch 17/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - loss: 0.0667
Epoch 18/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 4s 54ms/step - loss: 0.0629
Epoch 19/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 4s 57ms/step - loss: 0.0596
Epoch 20/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 4s 55ms/step - loss: 0.0589
Epoch 21/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 3s 19ms/step - loss: 0.0570
Epoch 22/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - loss: 0.0532
Epoch 23/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 3s 39ms/step - loss: 0.0477
Epoch 24/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 4s 51ms/step - loss: 0.0520
Epoch 25/25
70/70 ━━━━━━━━━━━━━━━━━━━━ 4s 56ms/step - loss: 0.0551
Out[62]:
<keras.src.callbacks.history.History at 0x255f6675a10>

Auto Encoder Summary

In [63]:
autoencoder.summary()

Model: "functional"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ input_layer (InputLayer)             │ (None, 17)                  │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense (Dense)                        │ (None, 7)                   │             126 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_1 (Dense)                      │ (None, 500)                 │           4,000 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_2 (Dense)                      │ (None, 500)                 │         250,500 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_3 (Dense)                      │ (None, 2000)                │       1,002,000 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_4 (Dense)                      │ (None, 10)                  │          20,010 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_5 (Dense)                      │ (None, 2000)                │          22,000 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_6 (Dense)                      │ (None, 500)                 │       1,000,500 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_7 (Dense)                      │ (None, 17)                  │           8,517 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 6,922,961 (26.41 MB)

 Trainable params: 2,307,653 (8.80 MB)

 Non-trainable params: 0 (0.00 B)

 Optimizer params: 4,615,308 (17.61 MB)

Weights

In [47]:
autoencoder.save_weights('autoencoder.h5')

Generate autoencoder values.

In [64]:
pred = encoder.predict(creditcard_df_scaled)
display (pd.DataFrame(pred))

280/280 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step
0 1 2 3 4 5 6 7 8 9
0 0.0 0.0 0.805977 0.243174 0.392488 0.0 0.000000 0.498317 0.107181 0.0
1 0.0 0.0 1.548790 2.200807 0.291019 0.0 0.160375 1.971252 0.622483 0.0
2 0.0 0.0 0.094488 0.317667 0.876340 0.0 0.377525 1.288587 1.669780 0.0
3 0.0 0.0 1.570302 0.147588 0.019113 0.0 0.588219 0.956700 0.482902 0.0
4 0.0 0.0 0.682093 0.204373 0.358395 0.0 0.016250 0.847094 0.354639 0.0
... ... ... ... ... ... ... ... ... ... ...
8945 0.0 0.0 4.269351 1.894183 4.817721 0.0 0.000000 1.834163 1.349662 0.0
8946 0.0 0.0 4.340765 2.187191 4.279402 0.0 0.000000 2.564641 0.984162 0.0
8947 0.0 0.0 4.516222 1.801927 4.091324 0.0 0.000000 1.940948 1.095353 0.0
8948 0.0 0.0 5.138627 0.969902 2.794135 0.0 0.000000 3.032073 2.081854 0.0
8949 0.0 0.0 5.205039 1.456889 3.564507 0.0 0.107596 2.988219 3.867671 0.0

8950 rows × 10 columns

Display Shape

In [65]:
pred.shape
Out[65]:
(8950, 10)

Create Elbow graph

In [67]:
scores_2 = []

range_values = range(1, 20)

for i in range_values:
  kmeans = KMeans(n_clusters= i)
  kmeans.fit(pred)
  scores_2.append(kmeans.inertia_)

plt.plot(scores_2, 'bx-')
plt.title('Finding right number of clusters')
plt.xlabel('Clusters')
plt.ylabel('scores') 
plt.show()
No description has been provided for this image

Display the graph, current and earlier wcss values

In [68]:
# Scores_1 – Earlier wcss values 
# Scores_2 – Current wcss values 
plt.plot(scores_1, 'bx-', color = 'r')
plt.plot(scores_2, 'bx-', color = 'g')
Out[68]:
[<matplotlib.lines.Line2D at 0x255b5fa5c50>]
No description has been provided for this image

Create K Means Cluster with optimal number of clusters. In this case it is 4 clusters

In [69]:
kmeans = KMeans(4)
kmeans.fit(pred)
labels = kmeans.labels_
y_kmeans = kmeans.fit_predict(creditcard_df_scaled)

Display Cluster Details

In [70]:
display(labels.shape) # Labels associated to each data point
display (labels.max())
display (labels.min())

(8950,)
3
0

Display individual cluster numbers

In [71]:
y_kmeans = kmeans.fit_predict(pred)
display(y_kmeans)

array([3, 3, 3, ..., 2, 2, 2])

Concatenate Cluster number with Original Data Set

In [72]:
df_cluster_dr = pd.concat([creditcard_df, pd.DataFrame({'cluster':labels})], axis = 1)
display(df_cluster_dr.head())
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE cluster
0 40.900749 0.818182 95.40 0.00 95.4 0.000000 0.166667 0.000000 0.083333 0.000000 0 2 1000.0 201.802084 139.509787 0.000000 12 1
1 3202.467416 0.909091 0.00 0.00 0.0 6442.945483 0.000000 0.000000 0.000000 0.250000 4 0 7000.0 4103.032597 1072.340217 0.222222 12 1
2 2495.148862 1.000000 773.17 773.17 0.0 0.000000 1.000000 1.000000 0.000000 0.000000 0 12 7500.0 622.066742 627.284787 0.000000 12 1
3 1666.670542 0.636364 1499.00 1499.00 0.0 205.788017 0.083333 0.083333 0.000000 0.083333 1 1 7500.0 0.000000 864.206542 0.000000 12 1
4 817.714335 1.000000 16.00 16.00 0.0 0.000000 0.083333 0.083333 0.000000 0.000000 0 1 1200.0 678.334763 244.791237 0.000000 12 1

Apply PCA on auto encoded data set

In [73]:
pca = PCA(n_components=2)
prin_comp = pca.fit_transform(pred)
pca_df = pd.DataFrame(data = prin_comp, columns =['pca1','pca2'])
display (pca_df.head())
pca1 pca2
0 -2.122130 0.065590
1 -0.203844 -1.023658
2 -1.028913 0.406165
3 -1.575889 -0.424091
4 -1.955709 -0.064391

Concatenate Cluster number with PCA Data Set

In [75]:
pca_df = pd.concat([pca_df,pd.DataFrame({'cluster':labels})], axis = 1)
pca_df.head()
Out[75]:
pca1 pca2 cluster
0 -2.122130 0.065590 1
1 -0.203844 -1.023658 1
2 -1.028913 0.406165 1
3 -1.575889 -0.424091 1
4 -1.955709 -0.064391 1

Print Clusters and number records in each cluster

In [76]:
pca_df.value_counts(pca_df.cluster)
Out[76]:
cluster
1    5497
2    1940
0    1296
3     217
Name: count, dtype: int64

Plot Graph

In [77]:
plt.figure(figsize=(10,10))
ax = sns.scatterplot(x="pca1", y="pca2", hue = "cluster", data = pca_df, palette =['red','green','blue','yellow'])
plt.show()
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In [ ]: